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- Publisher Website: 10.1109/TPWRS.2017.2694048
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Article: Fast Stability Scanning for Future Grid Scenario Analysis
Title | Fast Stability Scanning for Future Grid Scenario Analysis |
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Authors | |
Keywords | Clustering Feature selection Future grids Machine learning Scenario analysis |
Issue Date | 2018 |
Publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=59 |
Citation | IEEE Transactions on Power Systems, 2018, v. 33 n. 1, p. 514-524 How to Cite? |
Abstract | Future grid scenario analysis requires a major departure from conventional power system planning, where only a handful of most critical conditions is typically analyzed. To capture the interseasonal and temporal variations in the renewable generation of a future grid scenario necessitates the use of computationally intensive time-series analysis. In this paper, we propose a framework for fast stability scanning of future grid scenarios using an improved feature selection and self-adaptive PSO-k-means clustering algorithm. To achieve the computational speedup, the stability analysis is performed only on small number of representative cluster centroids instead of on the full set of operating conditions. As a case study, we perform small-signal stability and steady-state voltage stability scanning of a simplified model of the Australian national electricity market with significant penetration of renewable generation. The simulation results show the effectiveness of the proposed approach. Compared to an exhaustive time series scanning, the proposed framework reduced the computational burden up to ten times, with an acceptable level of accuracy. |
Persistent Identifier | http://hdl.handle.net/10722/263372 |
ISSN | 2023 Impact Factor: 6.5 2023 SCImago Journal Rankings: 3.827 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Liu, R | - |
dc.contributor.author | Verbič, G | - |
dc.contributor.author | Ma, J | - |
dc.contributor.author | Hill, DJ | - |
dc.date.accessioned | 2018-10-22T07:37:52Z | - |
dc.date.available | 2018-10-22T07:37:52Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | IEEE Transactions on Power Systems, 2018, v. 33 n. 1, p. 514-524 | - |
dc.identifier.issn | 0885-8950 | - |
dc.identifier.uri | http://hdl.handle.net/10722/263372 | - |
dc.description.abstract | Future grid scenario analysis requires a major departure from conventional power system planning, where only a handful of most critical conditions is typically analyzed. To capture the interseasonal and temporal variations in the renewable generation of a future grid scenario necessitates the use of computationally intensive time-series analysis. In this paper, we propose a framework for fast stability scanning of future grid scenarios using an improved feature selection and self-adaptive PSO-k-means clustering algorithm. To achieve the computational speedup, the stability analysis is performed only on small number of representative cluster centroids instead of on the full set of operating conditions. As a case study, we perform small-signal stability and steady-state voltage stability scanning of a simplified model of the Australian national electricity market with significant penetration of renewable generation. The simulation results show the effectiveness of the proposed approach. Compared to an exhaustive time series scanning, the proposed framework reduced the computational burden up to ten times, with an acceptable level of accuracy. | - |
dc.language | eng | - |
dc.publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=59 | - |
dc.relation.ispartof | IEEE Transactions on Power Systems | - |
dc.rights | IEEE Transactions on Power Systems. Copyright © Institute of Electrical and Electronics Engineers. | - |
dc.rights | ©20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | - |
dc.subject | Clustering | - |
dc.subject | Feature selection | - |
dc.subject | Future grids | - |
dc.subject | Machine learning | - |
dc.subject | Scenario analysis | - |
dc.title | Fast Stability Scanning for Future Grid Scenario Analysis | - |
dc.type | Article | - |
dc.identifier.email | Hill, DJ: dhill@eee.hku.hk | - |
dc.identifier.authority | Hill, DJ=rp01669 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TPWRS.2017.2694048 | - |
dc.identifier.scopus | eid_2-s2.0-85086632940 | - |
dc.identifier.hkuros | 293627 | - |
dc.identifier.volume | 33 | - |
dc.identifier.issue | 1 | - |
dc.identifier.spage | 514 | - |
dc.identifier.epage | 524 | - |
dc.identifier.isi | WOS:000418776400045 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 0885-8950 | - |